FRB/US Model

FRB/US model packages

The main FRB/US model package is a self-contained set of equations, data, programs and documentation that enables various types of simulations and provides information about the model's structure. The package contains the following files and subdirectories:

A readme file with basic information about the organization of the package and how to get started;

The subdirectory addins with EViews add-in commands that must be installed, following the instructions provided in the readme file;

The subdirectory data containing the EViews database used in simulating the model as described in the readme file;

The subdirectory subs containing two libraries of subroutines, including the library mce_solve used for solving FRB/US under model-consistent (rational) expectations in EViews; the RE Solver Package is available for those wishing to download the mce_solve software and documentation by itself;

Supply-side model

The program frbus_supply.prg estimates a multivariate state-space model that forms the basis of the FRB/US specification of potential output and its components. The zip file below contains the code and the latest data set used in estimation.

RE Solver Package

The software library mce_solve provides code for the solution of linear and non-linear models under model-consistent (MC) or “rational” (RE) expectations in EViews. This code is more reliable and efficient than the RE algorithm built into EViews (Fair-Taylor) at solving FRB/US when any of its expectations are MC. The mce_solve library includes two RE algorithms: E-Newton and E-QNewton. These algorithms iterate to find a model's RE solution with a sequence of updates to either exogenous estimates of the model's future-dated endogenous variables or exogenous components of such estimates. For single simulations of linear RE models, E-Newton is likely to be faster for models of small-to-medium size and E-QNewton is likely to be faster for larger models. For nonlinear models, E-Newton tends to be penalized relative to E-QNewton. The E-Newton algorithm has a substantial advantage over E-QNewton on experiments that involve a large number of RE solutions, as long as the same expectations Jacobian can be used for each E-Newton solution.